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Toward an Integrated Arctic Observing Network 2 Key Variables to Monitor in the Long Term In identifying key variables, the Committee was guided by its vision, expressed in Chapter 1, that an integrated Arctic Observing Network (AON) should provide easily accessible, timely, long-term, pan-arctic observations that detect the fundamental variations in the arctic system, can easily be compared and analyzed, and help improve understanding of how the arctic system functions and changes. The task of identifying key variables1 to monitor in such a broad, multidisciplinary network is challenging. Recognizing that a single discipline might take many years to settle on its own “key” variables, and that in some disciplines or communities these key variables have yet to be agreed upon, the Committee developed a purposely broad list of 31 key variables. Clearly, not all variables listed will be key to all disciplines or stakeholders. The list is expected to evolve and grow over time as the value of particular variables is demonstrated, as new questions arise, and as resources allow. The Committee’s intent with this list is to stimulate discussion. The Committee’s deliberations on key variables revealed that this approach is readily applied to physical variables but becomes harder in the biological and human dimensions realms. Similar difficulties have led some groups (e.g., Study of Environmental Arctic Change planners) to follow different paths toward setting measurement priorities. These alternate approaches focus on determining inter-relationships among variables, elucidating the workings of processes, or addressing key questions driving the science. A pertinent example of a scientific driver is the attribution of recent change. The AON will accommodate both question-driven observations and the key variables approach by integrating the data streams resulting from a variety of programs in a unified form. The AON is envisioned as a backbone of the observing activities that will outlive most of the theme-driven projects that contribute observations. On the other hand, the results from theme-driven programs will clarify which variables deserve inclusion in the AON. Many of the key variables identified here are already being measured at many sites. However, formulating the list of key variables can lead to observing system enhancements by identifying variables that are needed in many disciplines and by highlighting the need for available and accessible data on these variables. In particular, cross-disciplinary assessments of the need for pan-arctic datasets of these variables can help prioritize the network expansions that will serve the greatest needs in integrated monitoring. As illustration, assume that an observing station measures 500 variables including 10 that are “AON key variables.” The data related to these variables could be reported in a standardized way, undergo a quality control step, and then contribute to a pan-arctic dataset that is served through a single entry point—a central portal.2 An incentive for observatories to contribute their data to a pan-arctic network could be the knowledge that some of the variables they are measuring are important in an international context and that participation in the network could result in additional funding. In addition, they would be able to use the pan-arctic datasets for comparative studies. DEFINITIONS As the Committee worked to identify key variables, it recognized that the term caused some confusion and that, in fact, there were two terms of importance: key variable and 1 What is a “variable?” Zackenberg Station in northeast Greenland measures over 2,500 “variables.” If temperature is measured in the air and down a soil profile, this is considered several variables by those at Zackenberg. At Abisko, Sweden, temperature is considered to be one variable even if measured at 10 sites. For the purposes of this report, temperature is a single variable—whether measured in ocean, atmosphere, or at depth in the ground. In other words, the Committee’s usage of the term does not separate measurements of the same variable whether measured in the atmosphere, within permafrost, or in a human, for example. 2 Standardized observations and quality control for local and traditional knowledge components of the AON will take special consideration (see Chapter 5).
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Toward an Integrated Arctic Observing Network key indicator variable. A key variable is a variable that is fundamental and related to questions that are important throughout the Arctic, is essential to an overall understanding of the arctic system, and is also relevant at the local scale. A key variable is a necessary component of integrated monitoring because changes in associated variables cannot be understood without knowledge of changes in the key variable. The key variable is a disaggregated driver of lower-level changes.3 An example is temperature.4 A key indicator variable is a response variable or index that can be conveniently measured to denote changes in one or more key variables. Examples are phenology (the timing of events) and indices of human activity. For example, the proportion of whales harvested in open water by an arctic community during autumn rather than spring can be an indicator of climate-driven changes in sea ice cover. Key indicator variables may manifest themselves differently at different locations. In the framework to be used here, a key variable is a key variable whether its variations are obtained from direct measurements or by proxy methods. A proxy measurement of a key variable is an indirect estimation of the key variable, usually consisting of a measurement preserved in some physical manifestation. An example is tree ring width or density in fossil and modern wood samples: from the patterns of ring widths, one can infer variability in environmental variables such as, for example, temperature. The value of proxies to monitoring networks is that they provide historical context for the contemporary measurements and instrument records. They also improve data products that arise from reanalysis efforts. Beyond tree rings, other examples of proxies include ice and lake sediment cores and local and traditional knowledge of environmental history. Proxy records often share a common threat of being lost (perhaps through melting of an ice cap in which a climate record is preserved or through death of a village elder). Data in old formats are similarly at risk of being lost. WAYS OF GROUPING KEY VARIABLES Variables can be grouped in many ways. One approach is to organize variables according to underlying concepts. These concepts define the basic state of the physical, biological, chemical, and human environment, and identify and characterize natural variability and anthropogenic change. These concepts represent the most basic approach to system understanding and to the identification of the causes of change (e.g., physical-chemical drivers, human and biological drivers, environmental impacts, human and biological responses). Integrated monitoring is a particularly effective approach for recording these variables, and field manipulation experiments can be performed to obtain relationships among them. Such an approach is particularly useful for testing process formulations in models. This conceptual approach is the most consistent with the AON vision statement because this way of thinking provides an umbrella for the individual questions, hypotheses, and themes, and it facilitates a pan-arctic focus and long-term perspective. In addition, this approach is inclusive in its philosophy, cross-disciplinary, and represents a stable base that can evolve as key questions, access to the Arctic, technology, and human needs change. Although there are other ways of categorizing variables, such as organizing by problems or research theme, organizing by concept has the distinct advantage that the concepts are less likely to change over time than questions or themes. Thus, the Committee prefers an organization that is founded on state variables but presents other options to encourage discussion. These other options entail grouping by practicality and approach, grouping by synergies (i.e., creating benefits that only arise from a suite of measurements or combination of measurements and existing data), and grouping by timescale. Grouping by What Is Practical Groupings can be based on discipline, theme, and measurement approach. For example, variables can be classified by major disciplines: atmosphere, ocean, cryosphere, terrestrial. Themes that can provide the basis for classifications include biodiversity, land cover, and sustainable resource use. In terms of measurement approach, variables can be organized by platform (satellite, observatory, human observers, buoys, etc.), thereby maximizing the use of existing expertise and the cost-effectiveness of infrastructure. In addition, classifications can be responsive to initiatives that define their own information needs. Examples are the International Polar Year and the Arctic Climate Impact Assessment. These initiatives can provide structure and permanence to data. Finally, variables can be classified by stakeholder priorities such as the priorities of arctic communities. Grouping by Synergies A classification scheme can be based on one of a number of types of synergy. For example, synergies among existing and new monitoring activities can lead logically to groupings of variables. Synergies can also arise from better use of existing data (e.g., satellite images, photographs, biological samples that have untapped potential). For example, precipitation data collected by weather stations and then thrown 3 To be clear, this definition does not preclude a key variable from changing. 4 Consider the changing migration pattern of a caribou herd. The fundamental driver (key variable) could be temperature, which might be linked to changes in migration pattern because of its effect on vegetation phenology and composition, snow cover, and consequently the availability of food. These changes are lower-level responses that are driven by changes in the key variable temperature. Lower-level changes such as migration pattern are then indicators of changes in the key variable temperature.
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Toward an Integrated Arctic Observing Network away can instead be analyzed for pollutant chemistry, biological propagules, and isotopic composition. Improved interactions among existing networks can lead to synergies by revealing common needs or shared interests, for example, as can bridging of disciplines or domains such as physical science and local and traditional knowledge. Grouping by Timescale Classifications of variables can be based on the different timescales of measurements such as real-time (e.g., weather observations), seasonal (e.g., caribou locations, snowpack water equivalent), annual (e.g., permafrost temperature and active layer depth), decadal (e.g., land cover change), or longer timescales related to paleo studies. Another time-related differentiation is between “surprises” (i.e., unexpected occurrences) and steady change. Local observations can be a very cost-effective source of change detection and early warning (e.g., Chapin, 2005). Local observations can capture changes that are not easily tracked by measurements made at constant intervals or at low resolution. When grouping by timescale, it is essential to begin with a clear understanding of how the collected data will be organized because the observation strategy influences the sampling frequency (i.e., an intense decadal survey would perhaps rely on many mobile platforms whereas a permanent station network might be needed for the higher frequency monitoring). IDENTIFYING KEY VARIABLES Because the selection of key variables often depends on a particular context or application, many such variables have been identified for particular programs or for particular purposes by the planners for those activities (e.g., the Essential Climate Variables of the Global Climate Observing System). The Committee drew from these identified sets of variables the ones that, in the context of the preceding discussion, can be regarded as describing the basic state of and trends in the arctic environment. These variables support the AON vision. That is, they enable detection of fundamental variations in the arctic system, help improve understanding of how the arctic system functions and changes, and can easily be compared and analyzed. In further prioritizing the list, the Committee sought variables that, when they change, have major consequences for the arctic and/or global system and humanity. An important question that arises is “what is the appropriate number of key variables for this first phase of the AON?” Is it reasonable to consider the AON focusing on a small number of clearly important phenomena or must it include a wide range of variables? The Committee is charged to consider the “ideal” network and the “minimal” network, so is it reasonable to strive for a small total number of key variables? In the Committee’s view, it is unrealistic to include only a small number of variables when considering the arctic system as an integrated system, spanning an extremely broad spectrum of needed information, and for that reason Table 2.1 lists 31 variables. From a practical perspective, however, it is unlikely that pan-arctic datasets of all these variables can be produced with sufficient measurement density from the outset. There is a need to start with a reasonably limited subset and build incrementally from that point as resources allow and priorities evolve. Beginning with a subset of key variables could also show proof of concept and stimulate the necessary interest and resources to improve measurement density for these variables and gradually build up the number of key variables receiving focused attention. Given the preceding discussion, the question then becomes how to prioritize effort among the key variables for the first phase of the AON. The immediacy of an initial phase of the AON suggests three considerations in the assignment of priority: Key variables (or their proxies) that are at risk of being lost. This might include information in ice cores from diminishing ice caps or ice sheets, knowledge of elders, biological materials—for example, DNA of threatened species or long-term measurement records that are being terminated such as data from arctic river gauging stations or glacier mass balance networks. Reanalysis, where applicable, would guide where to focus effort. Key variables for which there are critical data gaps. Undoubtedly, some key variables have poorer coverage than others. Perhaps having particulary weak coverage among the key variables should move a variable higher up the priority list. Feasibility is also a factor in prioritizing which critical gap to fill. That is, could the available technology and logistics enable the gap-filling for certain key variables more readily than others? Key variables that have broad public appeal or are easily conveyed to the public. Examples include numbers of polar bears or extent of glacier retreat. These kinds of variables might help with broad buy-in to the value of the AON. SUMMARY TABLE Table 2.1 summarizes variables that have been identified by the Committee as an initial set of essential measurements for the AON. Because of the AON’s broad geographic and disciplinary scope, the list has 31 variables. The preceding discussion offers ideas for prioritizing effort among them. All of these key variables fulfill the criteria laid out in this chapter—they define the basic state of the environment and many of them can be used to improve understanding of the arctic system and identify and characterize change. The list
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Toward an Integrated Arctic Observing Network TABLE 2.1 The Arctic Observing Network’s 31 Key Variables and Key Indicator Variables Variablea Examples of Why the Variable Is Important Critical and Major Gaps in Observations (spatial, temporal, or thematic) PHYSICAL VARIABLES Albedo (K) Influences global change (through changes in cloud, land, and ocean cover—including ice and snow cover) Time sequences of fields of albedo, particularly in vegetated areas where there is masking, and also over ice Elevation/bathymetry (including shoreline) (K) A fundamental measure of shape of Earth’s surface Influences ocean and atmosphere circulation (including microclimate) Reveals coastal erosion Controls how materials are transported Important for glacier motion Potential hazards for transportation Whole Arctic Ocean (patchy coverage) Coverage at high resolution (elevation in coastal regions) Ice characteristics (including thickness, extent, and concentration) (K) Influences arctic energy balance Reservoir of stored fresh water Affects coastal erosion Influences marine and lacustrine transportation Affects biological habitat Affects hunting success Sea ice thickness everywhere Sea ice concentration in summer Sea ice extent in coastal areas Glacier thickness Permafrost thickness and ground ice concentration Precipitation (K) Controls biological community distribution Influences human water supply and causes droughts and flooding Arctic Ocean Topographically complex areas River systems smaller than the Arctic’s 10 largest systems Southeast Alaska to Prince William Sound Pressure (K) Driver of winds and ocean circulation, glacier motion (i.e., basal pressure) Marginal seas Central Arctic Ocean (below surface) Radiation (K) (spectral composition and fluxes from thermosphere to shallow ocean) Ultimate driver of weather and climate, biological activity, human health (e.g., UV damage) Spectrally resolved radiation Surface across entire arctic Salinity (K) Affects ocean density distribution and circulation Affects biological community distribution and populations Arctic Ocean Snow depth/water equivalent (K) Affects arctic energy balance Insulates underlying soils/sea ice Affects biological activity (e.g., caribou distributions, ringed seal reproduction) Affects winter transportation for humans Perennial sea ice Entire Arctic Ocean Distribution on land Soil moisture (K) Affects runoff, vegetation, biological productivity, terrestrial transportation Everywhere in subsurface areas Temperature (K) Direct measure of global warming Moderates all chemical and biochemical reactions Controls biological community boundaries Causes changes in permafrost that affect infrastructure Entire Arctic Ocean plus subarctic seas Atmosphere (especially above the first few meters) Terrestrial subsurface (particularly in the Central Siberia, Russian North East, and areas in Canada and China) Lack of year-round temperature in ocean Velocity (K) Feature of weather (storms, winds), ocean circulation, glacier motion, river runoff Arctic Ocean and marginal seas; height-resolved circulation above troposphere and over oceans
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Toward an Integrated Arctic Observing Network Variablea Examples of Why the Variable Is Important Critical and Major Gaps in Observations (spatial, temporal, or thematic) Water vapor concentration (including cloud properties) (K) Influences radiation budget (both up- and down-welling) through attenuation of UV-B Cloud and precipitation formation if aerosols are present Strongest radiatively active gas (i.e., more than carbon dioxide) Accelerates stratospheric ozone depletion if ice crystal deposition occurs Greenland Arctic Ocean Subarctic seas Freshwater flux (I) Influences ocean salinity and circulation Has impacts on fisheries, landscape change and human habitation and travel, wetland distribution Declining number of gauging stations plus not always at river mouth Glacier runoff Small rivers Contributions to Bering Strait Lake level (I) Affects human and other biological habitation and activity, water resources and fisheries, land use, lacustrine transportation A key land-water boundary and indicator of water balance Entire arctic Sea level (I) Influences coastal dynamics, human and other biological habitation and activity, oil and gas exploration, marine transportation A key land-water boundary and indicator of water balance Alaskan and Canadian coastline Russian Arctic: many sites are not operational Greenland—more than half of the Danish gauges in Greenland have been abandoned Aerosol concentration (K) (physical or biogeochemical variable) Influences air quality and human health, atmospheric energy balance, global climate, cloud formation Aerosol chemistry Limited beyond ARM sites (over time or space) Land cover (I) (Physical or biogeochemical variable) Influences habitat fragmentation, water balance, coastal erosion, transportation, animal migration, biological community boundary change, land use and management High resolution surface characteristics BIOGEOCHEMICAL VARIABLES Atmospheric chemistry and the contribution of trace gases (ozone, nitrous oxide, methane) (K) Influences human health (ozone) Most are radiatively active gasses Relevant to carbon sequestration Reveals nitrous oxide releases Reveals effects of land use/cover change Quantitative understanding of sources, sinks, and chemical processes in lower atmosphere Biodiversity (including species distributions) (I) Reveals natural and anthropogenic impacts on species richness and ecosystems, invasive species impacts, endangered species impacts Indicator of ecosystem structure Basic species list for the Arctic Genetic libraries Basic nomenclature Biomass (K) Relevant to food supply; ecosystem health, structure, and function; carbon sequestration and allocation; ocean color (i.e., by influencing albedo and thus transmission of light); Affects albedo by masking of snow Frequency and methods for assessing biomass on land, ocean, sea ice Carbon concentration (K) Impact on global warming (radiatively active gas—carbon dioxide, methane) Influences biological productivity, carbon sequestration, food web dynamics, ecosystem structure Terrestrial (including surface) observations and in biosphere (particularly in Russia and Canada) Winter coverage Nutrient concentration (K) Affects primary production, ecosystem structure and function, food webs/trophic interactions, energy fluxes Fundamental element of life Localized measurements (soils, vegetation, water) Understanding of function in biocomplex systems
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Toward an Integrated Arctic Observing Network Variablea Examples of Why the Variable Is Important Critical and Major Gaps in Observations (spatial, temporal, or thematic) Contaminant concentration (I) Affects human and animal health, water quality, atmospheric composition Indicator of anthropogenic activity and impacts U.S. arctic Critical chemical species Gaps in human levels Dissolved oxygen concentration (I) Indicator of biological production and exchange with the atmosphere Frequency of temporal coverage and density of spatial coverage Phenology, organismal behavior, and performance (I) Reveals changes in bud break, growing season, migratory timing, food availability for migrant birds, reproductive success, albedo, and carbon sequestration Indicator of timing and success of biological events Allometric data Biomass Reproductive success Hibernation ecology Tracer chemistry (natural tracers rather than localized tracer additions) (biogeochemical or physical variable) (I) Indicator of biogeochemical and physical processes, changes in pathways, climate-water interactions Reveals fundamental properties of aquatic systems Spatial and temporal patchiness throughout the arctic system Frequently, lack of multitracer approaches HUMAN-DIMENSION VARIABLES Human demographics (population size and structure (K), births, deaths, migration (I)) Impacted by and impacting climate change, resources, globalization, infrastructure, governance, resource availability and utilization, land use, capacity of ecosystems to support subsistence economy, patterns and variability in social change, population sizes/habitat fragmentation Disaggregated data by gender, indigenous/nonindigenous Regional gaps Health (e.g., birth weight, breast milk quality, cause of death, cultural health) (I) Help reveal quality of life, standard of living, human potential Indicator of human condition Mental health and diet Access to health care Quality of health care Regional gaps Cultural diversity (I) Help reveal quality of life, standard of living, human potential Indicator of human condition Cultural diversity (indigenous participation in government and research, languages in use, religious membership) Education (e.g., graduates, enrollment) (I) Human potential Indicator of human condition Access to education Understanding needs for education Economic indicators (e.g., employment, subsistence, government structure) (I) Help reveal quality of life, human-environment relations, social change Show effects of globalization and devolution of control to local people Indicator of human activity Assessment of new institutions in the Arctic Tracking employment opportunities Disaggregation of economic indicators (e.g., gender differences) NOTE: Column 1 lists the variable plus whether it is a key variable (K) or a key indicator variable (I). Column 2 gives examples of why the variable is important. Column 3 gives examples of major and critical gaps in observations. Variables are arranged alphabetically followed by assignment to one of three clusters—physical, biogeochemical, or human. There are intimate relationships among variables in the three clusters, so these assignments are not necessarily perfect or unchangeable. Furthermore, a continued discussion of the human variables in particular will require input from fields not included in this Committee, and more work will be needed to incorporate this dimension into the AON. The list is, as stated earlier, intended as a starting point for discussion. aK = key variable, I = key indicator variable.
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Toward an Integrated Arctic Observing Network is based on the Committee’s collective experience and expertise and information gathered during this study. Although the Committee’s composition introduces an inevitable subjective component to the compilation process, the list represents the Committee’s consensus and is intended to serve as a starting point for establishing AON measurement priorities. The table is divided into three clusters—physical, biogeochemical, and human dimensions. It became clear to the Committee that the “key variables” approach, although required by the charge given to the Committee, does not fit all aspects of the observing system equally. This approach is better suited to physical variables than to biogeochemical and human ones. The concept is loosened up for these later two categories, and in the future these components in particular will need further attention. The table also highlights critical gaps in observations. Because the table is a compilation of variables for which present observing networks are in widely different stages of evolution, the critical gaps vary from spatial or temporal details to thematic considerations. The latter are more common among the biogeochemical and human-dimension variables.
Representative terms from entire chapter: